Effective Brain Connectivity from Intracranial EEG Recordings: Identification of Epileptogenic Zone in Human Focal Epilepsies

2013 ◽  
pp. 87-101 ◽  
Author(s):  
Giulia Varotto ◽  
Laura Tassi ◽  
Fabio Rotondi ◽  
Roberto Spreafico ◽  
Silvana Franceschetti ◽  
...  
Keyword(s):  
Brain Connectivity ◽  
Epileptogenic Zone ◽  
Intracranial Eeg ◽  
Eeg Recordings

scholarly journals A Fuzzy Logic System for Seizure Onset Detection in Intracranial EEG

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Ahmed Fazle Rabbi ◽  
Reza Fazel-Rezai
Keyword(s):  
Fuzzy Rule ◽  
Epileptogenic Zone ◽  
Onset Detection ◽  
Seizure Onset ◽  
Intracranial Eeg ◽  
Fuzzy Algorithms ◽  
Remote Zone ◽  
Threshold Procedure ◽  
Eeg Recordings ◽  
Logic System

We present a multistage fuzzy rule-based algorithm for epileptic seizure onset detection. Amplitude, frequency, and entropy-based features were extracted from intracranial electroencephalogram (iEEG) recordings and considered as the inputs for a fuzzy system. These features extracted from multichannel iEEG signals were combined using fuzzy algorithms both in feature domain and in spatial domain. Fuzzy rules were derived based on experts' knowledge and reasoning. An adaptive fuzzy subsystem was used for combining characteristics features extracted from iEEG. For the spatial combination, three channels from epileptogenic zone and one from remote zone were considered into another fuzzy subsystem. Finally, a threshold procedure was applied to the fuzzy output derived from the final fuzzy subsystem. The method was evaluated on iEEG datasets selected from Freiburg Seizure Prediction EEG (FSPEEG) database. A total of 112.45 hours of intracranial EEG recordings was selected from 20 patients having 56 seizures was used for the system performance evaluation. The overall sensitivity of 95.8% with false detection rate of 0.26 per hour and average detection latency of 15.8 seconds was achieved.

Localization of Epileptogenic Zone on Pre-surgical Intracranial EEG Recordings: Toward a Validation of Quantitative Signal Analysis Approaches

2014 ◽  
Vol 28 (6) ◽  
pp. 832-837 ◽  
Author(s):  
Ralph G. Andrzejak ◽  
Olivier David ◽  
Vadym Gnatkovsky ◽  
Fabrice Wendling ◽  
Fabrice Bartolomei ◽  
...  
Keyword(s):  
Signal Analysis ◽  
Epileptogenic Zone ◽  
Intracranial Eeg ◽  
Eeg Recordings

scholarly journals Utilization of independent component analysis for accurate pathological ripple detection in intracranial EEG recordings recorded extra- and intra-operatively

2018 ◽  
Vol 129 (1) ◽  
pp. 296-307 ◽  
Author(s):  
Shoichi Shimamoto ◽  
Zachary J. Waldman ◽  
Iren Orosz ◽  
Inkyung Song ◽  
Anatol Bragin ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Intracranial Eeg ◽  
Eeg Recordings

scholarly journals The Modular Organization of Pain Brain Networks: An fMRI Graph Analysis Informed by Intracranial EEG

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Camille Fauchon ◽  
David Meunier ◽  
Isabelle Faillenot ◽  
Florence B Pomares ◽  
Hélène Bastuji ◽  
...  
Keyword(s):  
Information Integration ◽  
Functional Organization ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Modular Organization ◽  
Noxious Heat ◽  
Intracranial Eeg ◽  
Brain Connectome ◽  
Posterior Parietal ◽  
The Brain

Abstract Intracranial EEG (iEEG) studies have suggested that the conscious perception of pain builds up from successive contributions of brain networks in less than 1 s. However, the functional organization of cortico-subcortical connections at the multisecond time scale, and its accordance with iEEG models, remains unknown. Here, we used graph theory with modular analysis of fMRI data from 60 healthy participants experiencing noxious heat stimuli, of whom 36 also received audio stimulation. Brain connectivity during pain was organized in four modules matching those identified through iEEG, namely: 1) sensorimotor (SM), 2) medial fronto-cingulo-parietal (default mode-like), 3) posterior parietal-latero-frontal (central executive-like), and 4) amygdalo-hippocampal (limbic). Intrinsic overlaps existed between the pain and audio conditions in high-order areas, but also pain-specific higher small-worldness and connectivity within the sensorimotor module. Neocortical modules were interrelated via “connector hubs” in dorsolateral frontal, posterior parietal, and anterior insular cortices, the antero-insular connector being most predominant during pain. These findings provide a mechanistic picture of the brain networks architecture and support fractal-like similarities between the micro-and macrotemporal dynamics associated with pain. The anterior insula appears to play an essential role in information integration, possibly by determining priorities for the processing of information and subsequent entrance into other points of the brain connectome.

scholarly journals Zero-crossing patterns reveal subtle epileptiform discharges in the scalp EEG

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jan Pyrzowski ◽  
Jean- Eudes Le Douget ◽  
Amal Fouad ◽  
Mariusz Siemiński ◽  
Joanna Jędrzejczak ◽  
...  
Keyword(s):  
Normal Subjects ◽  
Intracranial Eeg ◽  
Zero Crossing ◽  
Temporal Precision ◽  
Scalp Eeg ◽  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges ◽  
Eeg Recordings ◽  
Patients With Epilepsy ◽  
Low Amplitude

AbstractClinical diagnosis of epilepsy depends heavily on the detection of interictal epileptiform discharges (IEDs) from scalp electroencephalographic (EEG) signals, which by purely visual means is far from straightforward. Here, we introduce a simple signal analysis procedure based on scalp EEG zero-crossing patterns which can extract the spatiotemporal structure of scalp voltage fluctuations. We analyzed simultaneous scalp and intracranial EEG recordings from patients with pharmacoresistant temporal lobe epilepsy. Our data show that a large proportion of intracranial IEDs manifest only as subtle, low-amplitude waveforms below scalp EEG background and could, therefore, not be detected visually. We found that scalp zero-crossing patterns allow detection of these intracranial IEDs on a single-trial level with millisecond temporal precision and including some mesial temporal discharges that do not propagate to the neocortex. Applied to an independent dataset, our method discriminated accurately between patients with epilepsy and normal subjects, confirming its practical applicability.

scholarly journals CNN for a Connectivity Based Epilepsy Diagnosis with Resting-state EEG

2021 ◽  
Author(s):  
Berjo Rijnders ◽  
Emin Erkan Korkmaz ◽  
Funda Yildirim
Keyword(s):  
Resting State ◽  
Cognitive Abilities ◽  
Brain Connectivity ◽  
Clinical Decision ◽  
Control Group ◽  
Learning To Learn ◽  
Graph Metrics ◽  
Epilepsy Diagnosis ◽  
Eeg Recordings ◽  
Surface Eeg

Objective: This study investigates the performance of a CNN algorithm on epilepsy diagnosis. Without pathology, diagnosis involves long and costly electroencephalographic (EEG) monitoring. Novel approaches may overcome this by comparing brain connectivity using graph metrics. This study, however, uses deep learning to learn connectivity patterns directly from easily acquired EEG data. Approach: A convolutional neural network (CNN) algorithm was applied on directed Granger causality (GC) connectivity measures, derived from 50 seconds of resting-state surface EEG recordings from 30 subjects with epilepsy and a 30 subject control group. Main results: The learned CNN filters reflected reduced delta band connectivity in frontal regions and increased left lateralized frontal-posterior gamma band connectivity. A diagnosis accuracy of 85% (F1-score 85%) was achieved by an ensemble of CNN models, each trained on differently prepared data from different electrode combinations. Conclusions: Appropriate preparation of connectivity data enables generic CNN algorithms to be used for detection of multiple discriminative epileptic features. Differential patterns revealed in this study may help to shed light on underlying altered cognitive abilities in epilepsy patients. Significance: The accuracy achieved in this study shows that, in combination with other methods, this approach could prove a valuable clinical decision support system for epilepsy diagnosis.

A Robust Coherence-Based Brain Connectivity Method with an Application to EEG Recordings

2016 ◽  
pp. 339-344
Author(s):  
Jiaqing Yan ◽  
Jianbin Wen ◽  
Yinghua Wang ◽  
Xianzeng Liu ◽  
Xiaoli Li
Keyword(s):  
Brain Connectivity ◽  
Eeg Recordings

Physiological Activity and Artefacts in Epileptic Brain in Subdural EEG

2018 ◽  
pp. 84-97
Author(s):  
Beate Diehl ◽  
Catherine A. Scott
Keyword(s):  
Motor Cortex ◽  
Brain Region ◽  
Physiological Activity ◽  
Brain Oscillations ◽  
Intracranial Eeg ◽  
Scalp Eeg ◽  
Focal Pattern ◽  
Mu Rhythms ◽  
Eeg Recordings ◽  
Central Pacemaker

‘Physiological activity and artefacts in epileptic brain in subdural EEG’ reviews intracranial appearances of physiological brain rhythms in each brain region, many of which are also seen on scalp EEG. The alpha rhythm has been described as originating from multiple occipital and extra-occipital cortical generators variously overlapping and influencing each other, probably under the relative control of a central pacemaker. Another more focal pattern has been described in intracranial EEG recordings in the calcarine region, with a third rhythm arising in midtemporal regions, not detectable in scalp EEG, with a frequency in the alpha or theta range. Lambda waves, sleep structures, and mu rhythms over motor cortex can also be detected on subdural electrodes. On a region-by-region basis, intracranial EEG appearances are summarized, including brain oscillations in hippocampus and motor cortex and their modifiers, as well as ongoing rhythms in cingulum. Common sources of physiological and non-physiological artefacts are reviewed.

The correlation of magnetoencephalography to intracranial EEG in localizing the epileptogenic zone: A study of the surgical resection outcome

2014 ◽  
Vol 108 (9) ◽  
pp. 1581-1590 ◽  
Author(s):  
Salah Almubarak ◽  
Andreas Alexopoulos ◽  
Felix Von-Podewils ◽  
Z. Irene Wang ◽  
Yosuke Kakisaka ◽  
...  
Keyword(s):  
Surgical Resection ◽  
Epileptogenic Zone ◽  
Intracranial Eeg
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